This section of this document is intended to introduce various aspects of art that may be related to various aspects of the present invention described and/or claimed below. This section provides background information to facilitate a better understanding of the various aspects of the present invention. It should be understood that the statements in this section of this document are to be read in this light, and not as admissions of prior art.
This invention relates generally to diagnostic systems and more specifically to diagnostic systems and methods that perform diagnostic functions at optimal times and that generate diagnostic warnings in an optimal fashion.
Unless indicated otherwise, in order to simplify this explanation, the present invention will be described hereinafter in the context of the industrial automation industry. Nevertheless, it should be appreciated that the present invention includes various methods and apparatus that may be used in any of several different industries including, but not limited to, industrial automation and building automation as well as the medical field and other businesses where electronic and/or mechanical resource operations are analyzed to determine if tell tale signs of interesting conditions (e.g., a likely failure condition or an unexpected condition) occur.
Many industries employ complex automated manufacturing systems that include hundreds and even thousands of different electronic and mechanical resources that are integrated into machine lines for performing manufacturing processes. Most electronic and mechanical resources and systems have an expected useful life after which some or all of the components have to be replaced or repaired. In addition, most resources may malfunction prematurely under certain operating conditions or due to imperfections in the resources when the resources themselves were manufactured.
As with most electronic and mechanical devices, the useful lives of machine line resources can be extended via proper and routine maintenance. To this end, many large manufacturing concerns employ a number of different maintenance engineers charged with routinely maintaining resources. Here, in the case of mechanical resources maintenance may simply include keeping resources well lubricated and periodically replacing worn components. In the case of electronic components and some mechanical components, maintenance may include diagnostically analyzing operating data during normal operation of the resources. For example, for a specific set of resources there may be a range of acceptable operating parameters. A trend over several weeks toward one end or the other of the acceptable range of operating parameters may indicate a likely pending failure of certain components. When a trend is slow, prior experience may indicate that the likely time prior to failure will be relatively long and, when a trend is rapid, experience may indicate that failure is imminent. In some cases diagnostic processes may also include specific operations over and above normal resource operating procedures and analysis of resulting diagnostic data.
Experience generally guides development of diagnostic processes. For example, in the case of a first resource sub-set, experience may indicate that trend data should be obtained and analyzed on a weekly basis to avoid malfunctions and downtime whereas, in the case of a second resource sub-set, experience may indicate that trend data should be obtained and analyzed on a monthly basis.
As the number of manufacturing lines within facilities become greater, the number of diagnostic processes required to service facility resources increases. In addition, as the machine lines become more complex, the complexity of diagnostic processes also often increases. Moreover, as diagnostic results are examined, new diagnostic procedures are often developed that take into account new trends in diagnostic results and, in some cases, normal system operating data.
While diagnostic processes are advantageous and necessary, unfortunately diagnostic requirements increase manufacturing costs appreciably. To this end, large manufacturing concerns often include a large number of different resource types integrated into many different machine lines where the diagnostics required for each of the lines may be unique to the specific machine line. Here, maintenance engineers have to be relatively highly skilled in order to provide services to all lines within a concern's facilities. In addition, in most cases, diagnostic results do not warrant immediate maintenance. Thus, in cases where an engineer has to be present to perform diagnostic processes, if the engineer's presence is not immediately required to address problems related to the diagnostic results, valuable engineer time is wasted.
Many manufacturing concerns have reduced maintenance engineer or technician training requirements by training specific engineers to service specific machine lines and/or resources. For instance, a large manufacturing concern having twenty manufacturing facilities and many buildings at each facility may employ a total of ten maintenance engineers including two engineers in each of five different maintenance classes. Here each engineer need only be versed in maintaining one fifth of the entire set of resources employed by the concern. A specific engineer may routinely work at a different facility each day of a two week cycle and may be on call to address specific unforeseen interesting conditions in any of the facilities when the conditions occur.
To reduce the amount of time engineers have to spend performing routine diagnostic processes, in some cases diagnostic processes have been automated. For example, where a diagnostic process must be performed every week on a resource sub-set, a controller for the resource sub-set may be programmed to automatically perform the process at 5 AM every Monday morning. Similarly, where a process has to be performed every month, the controller may be programmed to automatically perform the process at 5 AM on the first of every month. In these cases either the controller or some other processor is programmed to examine the diagnostic results and, where an interesting condition occurs, to indicate that the interesting condition occurred. In any event, the diagnostic results may be stored for processing via subsequent trend type diagnostic analysis.
Automated diagnostics and multiple classes of maintenance engineers have solved many of the problems associated with maintenance programs. Nevertheless, some shortcomings still exist. For example, where an automatic diagnostic process is performed at 5 AM on the first of every month and a maintenance engineer is in a specific facility on February 27th and is not scheduled to be back in the facility until one week later, if the diagnostic process on March 1 indicates an interesting condition that requires consideration by an engineer, the engineer will have to make an additional and unforeseen trip back to the facility on March 1. In addition to wasting travel time, the additional trip may also throw off the engineer's regular schedule.
As another example, when an interesting condition is identified, often a signal is sent to a central facility or concern monitoring or service station which then contacts an engineer to resolve the condition. Here, in most cases, a monitoring employee must assess the situation, identify a qualified engineer to address the condition and then issue a work request of some type to the engineer. This process requires that the monitoring employee be familiar with engineer qualifications which is not always the case—especially in the case of large concerns where engineer and monitoring employee turnover may be routine.
In addition, at any given time there is usually a specific engineer within the sub-set of qualified engineers that is optimal for addressing a specific interesting condition and known systems fail to enable the monitoring employee to identify the optimal engineer. In this regard, all other things being equal, the qualified engineer that is currently least busy should address an occurring interesting condition. Similarly, all other things being equal, the qualified engineer that is currently closest to the location of the interesting condition should address the occurring interesting condition. In addition, all other things being equal, if an interesting condition occurs at a facility location where a qualified engineer is scheduled to be within a short period to perform other maintenance duties, that engineer may be the optimal engineer to address the condition. Known current systems do not enable a monitoring employee to optimally assign tasks to maintenance engineers as a function of various factors such as proximity, qualifications, availability, etc.
One other problem that has been recognized with existing diagnostic systems is that often there are broad location related trends that cannot be appreciated at the resource level. To this end, where a rash of interesting conditions occurs within a specific area of a facility there may be some environmental cause (e.g., temperature, excess humidity, magnetic field, etc.) that is affecting resource operations. Current known systems have no way of grouping together location based interesting conditions.
Yet one other problem with addressing diagnostically interesting conditions is related to mobile resources. One recent trend in manufacturing resources is to provide resources that are rapidly reconfigurable so that many different products can be manufactured using different integrated resource subsets or so that one resource may be used at different times with different resource sub-sets. For example, a dryer machine may be wheeled between several facility locations to be used at different times with different plastic molding resource sub-assemblies. Here, where interesting conditions are noticed to service stations, known systems fail to provide a mechanism for determining which of several different stations should receive notice of interesting conditions associated with the mobile resource.